The training procedure is shown in Alg. 1. There are 2 types of tasks in our proposed ST approach. The support set inputs inner tasks to update the inner network, and the traditional few-shot task is trained as the outer task. There are the same metric-based few-shot methods in the outer task and inner task but in different parameter update ways. Our ST approach can work on metric-based few-shot methods, i.e., MatchingNet [1]}, ProtoNet [2]}, RelationNet [3]}. Although graph-based methods are superior in performance, they can not deal with the varying number of query samples by the same network. Therefore, our ST approach is mainly designed on the metric-based approaches. We introduce our method by taking ProtoNet as an example, where \(f_\phi \)  and \(f_\mu \)  are the inner network and outer network with the same architecture and initial parameters.
